
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class   lowerCAmelCase__	(      lowercase__     ):
		a__			:		Any     =						["""image_processor""", """tokenizer"""]
		a__			:		Union[str, Any]     =						"""BlipImageProcessor"""
		a__			:		List[str]     =						"""AutoTokenizer"""






		def __init__(						self						:				str				,     SCREAMING_SNAKE_CASE__						:				str				,     SCREAMING_SNAKE_CASE__						:				Optional[Any]				,     SCREAMING_SNAKE_CASE__						:				Tuple    )	->				Dict:
						super().__init__(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_    )

						# add QFormer tokenizer
						__lowerCamelCase          =      qformer_tokenizer






		def __call__(						self						:				Union[str, Any]				,     SCREAMING_SNAKE_CASE__						:				ImageInput = None				,     SCREAMING_SNAKE_CASE__						:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,     SCREAMING_SNAKE_CASE__						:				bool = True				,     SCREAMING_SNAKE_CASE__						:				Union[bool, str, PaddingStrategy] = False				,     SCREAMING_SNAKE_CASE__						:				Union[bool, str, TruncationStrategy] = None				,     SCREAMING_SNAKE_CASE__						:				Optional[int] = None				,     SCREAMING_SNAKE_CASE__						:				int = 0				,     SCREAMING_SNAKE_CASE__						:				Optional[int] = None				,     SCREAMING_SNAKE_CASE__						:				Optional[bool] = None				,     SCREAMING_SNAKE_CASE__						:				bool = False				,     SCREAMING_SNAKE_CASE__						:				bool = False				,     SCREAMING_SNAKE_CASE__						:				bool = False				,     SCREAMING_SNAKE_CASE__						:				bool = False				,     SCREAMING_SNAKE_CASE__						:				bool = False				,     SCREAMING_SNAKE_CASE__						:				bool = True				,     SCREAMING_SNAKE_CASE__						:				Optional[Union[str, TensorType]] = None				,     **SCREAMING_SNAKE_CASE__						:				List[Any]				,     )	->				BatchFeature:


						if images is None and text is None:
										raise ValueError('''You have to specify at least images or text.'''    )

						__lowerCamelCase          =      BatchFeature()

						if text is not None:
										__lowerCamelCase          =      self.tokenizer(
										    text=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_				,     padding=SCREAMING_SNAKE_CASE_				,     truncation=SCREAMING_SNAKE_CASE_				,     max_length=SCREAMING_SNAKE_CASE_				,     stride=SCREAMING_SNAKE_CASE_				,     pad_to_multiple_of=SCREAMING_SNAKE_CASE_				,     return_attention_mask=SCREAMING_SNAKE_CASE_				,     return_overflowing_tokens=SCREAMING_SNAKE_CASE_				,     return_special_tokens_mask=SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     return_token_type_ids=SCREAMING_SNAKE_CASE_				,     return_length=SCREAMING_SNAKE_CASE_				,     verbose=SCREAMING_SNAKE_CASE_				,     return_tensors=SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_				,     )
										encoding.update(SCREAMING_SNAKE_CASE_    )
										__lowerCamelCase          =      self.qformer_tokenizer(
										    text=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_				,     padding=SCREAMING_SNAKE_CASE_				,     truncation=SCREAMING_SNAKE_CASE_				,     max_length=SCREAMING_SNAKE_CASE_				,     stride=SCREAMING_SNAKE_CASE_				,     pad_to_multiple_of=SCREAMING_SNAKE_CASE_				,     return_attention_mask=SCREAMING_SNAKE_CASE_				,     return_overflowing_tokens=SCREAMING_SNAKE_CASE_				,     return_special_tokens_mask=SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     return_token_type_ids=SCREAMING_SNAKE_CASE_				,     return_length=SCREAMING_SNAKE_CASE_				,     verbose=SCREAMING_SNAKE_CASE_				,     return_tensors=SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_				,     )
										__lowerCamelCase          =      qformer_text_encoding.pop('''input_ids'''    )
										__lowerCamelCase          =      qformer_text_encoding.pop('''attention_mask'''    )

						if images is not None:
										__lowerCamelCase          =      self.image_processor(SCREAMING_SNAKE_CASE_				,     return_tensors=SCREAMING_SNAKE_CASE_    )
										encoding.update(SCREAMING_SNAKE_CASE_    )

						return encoding






		def 					__A			(						self						:				Dict				,     *SCREAMING_SNAKE_CASE__						:				str				,     **SCREAMING_SNAKE_CASE__						:				str    )	->				int:
						return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_    )






		def 					__A			(						self						:				Union[str, Any]				,     *SCREAMING_SNAKE_CASE__						:				int				,     **SCREAMING_SNAKE_CASE__						:				Optional[int]    )	->				Dict:
						return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_    )






		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 					__A			(						self						:				List[Any]    )	->				Optional[int]:
						__lowerCamelCase          =      self.tokenizer.model_input_names
						__lowerCamelCase          =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )






		def 					__A			(						self						:				Optional[int]				,     SCREAMING_SNAKE_CASE__						:				Optional[Any]				,     **SCREAMING_SNAKE_CASE__						:				str    )	->				str:


						if os.path.isfile(SCREAMING_SNAKE_CASE_    ):
										raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''    )
						os.makedirs(SCREAMING_SNAKE_CASE_				,     exist_ok=SCREAMING_SNAKE_CASE_    )
						__lowerCamelCase          =      os.path.join(SCREAMING_SNAKE_CASE_				,     '''qformer_tokenizer'''    )
						self.qformer_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_    )
						return super().save_pretrained(SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_    )







		@classmethod
		def 					__A			(						cls						:				List[Any]				,     SCREAMING_SNAKE_CASE__						:				Optional[Any]				,     **SCREAMING_SNAKE_CASE__						:				Optional[int]    )	->				int:
						__lowerCamelCase          =      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_				,     subfolder='''qformer_tokenizer'''    )
						__lowerCamelCase          =      cls._get_arguments_from_pretrained(SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_    )
						args.append(SCREAMING_SNAKE_CASE_    )
						return cls(*SCREAMING_SNAKE_CASE_    )





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase        =				{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RobertaPreLayerNormForCausalLM""",
					    """RobertaPreLayerNormForMaskedLM""",
					    """RobertaPreLayerNormForMultipleChoice""",
					    """RobertaPreLayerNormForQuestionAnswering""",
					    """RobertaPreLayerNormForSequenceClassification""",
					    """RobertaPreLayerNormForTokenClassification""",
					    """RobertaPreLayerNormModel""",
					    """RobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRobertaPreLayerNormForCausalLM""",
					    """TFRobertaPreLayerNormForMaskedLM""",
					    """TFRobertaPreLayerNormForMultipleChoice""",
					    """TFRobertaPreLayerNormForQuestionAnswering""",
					    """TFRobertaPreLayerNormForSequenceClassification""",
					    """TFRobertaPreLayerNormForTokenClassification""",
					    """TFRobertaPreLayerNormMainLayer""",
					    """TFRobertaPreLayerNormModel""",
					    """TFRobertaPreLayerNormPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase        =				[
					    """FlaxRobertaPreLayerNormForCausalLM""",
					    """FlaxRobertaPreLayerNormForMaskedLM""",
					    """FlaxRobertaPreLayerNormForMultipleChoice""",
					    """FlaxRobertaPreLayerNormForQuestionAnswering""",
					    """FlaxRobertaPreLayerNormForSequenceClassification""",
					    """FlaxRobertaPreLayerNormForTokenClassification""",
					    """FlaxRobertaPreLayerNormModel""",
					    """FlaxRobertaPreLayerNormPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roberta_prelayernorm import (
					    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    RobertaPreLayerNormConfig,
					    RobertaPreLayerNormOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roberta_prelayernorm import (
										    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RobertaPreLayerNormForCausalLM,
										    RobertaPreLayerNormForMaskedLM,
										    RobertaPreLayerNormForMultipleChoice,
										    RobertaPreLayerNormForQuestionAnswering,
										    RobertaPreLayerNormForSequenceClassification,
										    RobertaPreLayerNormForTokenClassification,
										    RobertaPreLayerNormModel,
										    RobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roberta_prelayernorm import (
										    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRobertaPreLayerNormForCausalLM,
										    TFRobertaPreLayerNormForMaskedLM,
										    TFRobertaPreLayerNormForMultipleChoice,
										    TFRobertaPreLayerNormForQuestionAnswering,
										    TFRobertaPreLayerNormForSequenceClassification,
										    TFRobertaPreLayerNormForTokenClassification,
										    TFRobertaPreLayerNormMainLayer,
										    TFRobertaPreLayerNormModel,
										    TFRobertaPreLayerNormPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roberta_prelayernorm import (
										    FlaxRobertaPreLayerNormForCausalLM,
										    FlaxRobertaPreLayerNormForMaskedLM,
										    FlaxRobertaPreLayerNormForMultipleChoice,
										    FlaxRobertaPreLayerNormForQuestionAnswering,
										    FlaxRobertaPreLayerNormForSequenceClassification,
										    FlaxRobertaPreLayerNormForTokenClassification,
										    FlaxRobertaPreLayerNormModel,
										    FlaxRobertaPreLayerNormPreTrainedModel,
										)

else:
					import sys

					UpperCAmelCase        =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


